Quantum computing is fundamentally limited by the Planck constant ( \(h = 6.63\ \times {{10}^{ - 34}}\ J \cdot s\) ) through the Heisenberg Limit. The energy consumption over a given time, or the speed of processing information with a specific energy budget, is a core research focus in quantum computing. To date, the smallest action (the energy-time cost) achieved is approximately \({{10}^{ - 29}}J \cdot s\) , using a giant spin qubit composed of 20 spins. In our study, we achieved an action of \(1.66\ \times {{10}^{ - 34}}\ J \cdot s\) to reversibly manipulate a single spin qubit through a spin-spin magnetic interaction experiment. By adhering to the principle of least action, our theoretical and experimental results establish the minimal action required. Our findings highlight the potential of spin-qubit quantum computers as accelerators for computation-intensive applications, such as AI and Post-Quantum Cryptography, since they exhibit several unique advantages: 1. High energy efficiency (by approaching the Heisenberg limit as well as the Landauer bound); 2. High-density integration (with just an atom/ion per qubit); 3. Long coherence times (tens of seconds); 4. High-fidelity (98%); and 5. Fault tolerance (through decoherence-free subspaces).
Quantum computing is fundamentally limited by the Planck constant ( (h = 6.63\ \times {{10}^{ - 34}}\ J \cdot s) ) through the Heisenberg Limit. The energy consumption over a given time, or the speed of processing information with a specific energy budget, is a core research focus in quantum computing. To date, the smallest action (the energy-time cost) achieved is approximately ({{10}^{ - 29}}J \cdot s) , using a giant spin qubit composed of 20 spins. In our study, we achieved an action of (1.66\ \times {{10}^{ - 34}}\ J \cdot s) to reversibly manipulate a single spin qubit through a spin-spin magnetic interaction experiment. By adhering to the principle of least action, our theoretical and experimental results establish the minimal action required. Our findings highlight the potential of spin-qubit quantum computers as accelerators for computation-intensive applications, such as AI and Post-Quantum Cryptography, since they exhibit several unique advantages: 1. High energy efficiency (by approaching the Heisenberg limit as well as the Landauer bound); 2. High-density integration (with just an atom/ion per qubit); 3. Long coherence times (tens of seconds); 4. High-fidelity (98%); and 5. Fault tolerance (through decoherence-free subspaces).
Semiconductor spin qubits offer a promising route to compact and scalable quantum computers, combining long coherence times in isotopically purified silicon with compatibility with semiconductor manufacturing. Beyond computational speedup, their energy efficiency is an important consideration for future large-scale systems. We evaluate the power and energy requirements of a Loss-DiVincenzo spin-qubit quantum computer by analyzing the Quantum Fourier Transform (QFT) and benchmarking it against the classical Fast Fourier Transform (FFT). We develop a detailed energy budget, identifying cryogenic cooling as the dominant baseline contribution before quantum error correction (QEC). The comparison between the QFT and the classical FFT reveals that, for any quantum workload operating under a polynomially bounded sampling regime, the crossover in energy consumption occurs at smaller problem sizes than the crossover in execution time. Thus, quantum computing can become energetically competitive before achieving a speedup in execution time. We then incorporate surface-code QEC and analyze the energy costs of QEC cycles, gate synthesis, and decoding. At scale, instrumentation and decoding become dominant contributions, while cryogenic cooling imposes a constraint on scalability. We show that optimizing the control-signal demultiplexing ratio can reduce the overall energy cost by approximately two orders of magnitude. For a system with about one million physical qubits, we estimate a power consumption of approximately 100 kW, at least one order of magnitude below that of today's highest-performance classical computing systems.
Qubits can be made out of a variety of experimental platforms, fig. 1. Small processors have already been demonstrated in trapped ions and superconductors but in both cases, the constraints for coherent manipulation and the effective large size of the qubits including all quantum functionalities are hardly compatible with very large-scale architecture. When it comes to the crucial issue of large-scale integration, Si spin qubits are well positioned due to their long coherence times and compatibility with semiconductor fabrication techniques. Indeed, dephasing times of the order of 1s have been achieved in Si-based spin qubits and single-qubit gate fidelities are high enough for scalable, fault-tolerant quantum computing.
Quantum computation has emerged as a prominent research field with the potential to revolutionize information processing. Recent advancements have focused on extending qubit coherence times and enhancing error correction and fault-tolerant protocols, achieving coherence time to gate time ratios as high as 10⁸ and reducing error rates to as low as 0.143% ± 0.003% per cycle for 101 qubit system. The next critical challenge lies in scaling up qubit systems to support large-scale quantum computation that exceeds the performance of classical supercomputer. In this study, we summarize the working mechanisms, physical properties, gate operations, and limitations of three leading qubit platforms—superconducting qubits, trapped-ion qubits, and neutral atom qubits. Through a systematic comparison, we identify the key challenges that hinder qubit system scalability. Finally, we outline future research directions to address these challenges, including exploring the fundamental physics underpinning long coherence times, improving control tools such as laser systems, enhancing material quality and fabrication precision, and investigating hybrid quantum architectures. We hope this study offers a valuable perspective for researchers working toward the development of scalable and reliable quantum computing systems.
Quantum computing represents a transformative paradigm that exploits the principles of quantum mechanics—superposition, entanglement, and quantum interference—to perform computations beyond the capabilities of classical computers. This review presents a comprehensive overview of the fundamental concepts underlying quantum computation, including qubits, quantum gates, quantum algorithms and measurement theory. It further examines major physical implementations of quantum computers, such as superconducting circuits, trapped ions, photonic systems, spin-based qubits, and topological approaches, highlighting their operational principles, advantages, and technological limitations. Key challenges hindering large-scale quantum computation—decoherence, error rates, qubit scalability, and fault tolerance—are critically discussed. Additionally, recent progress in quantum error correction, hybrid quantum–classical algorithms, and hardware optimization is summarized. The review concludes by outlining future research directions and potential applications of quantum computing in cryptography, materials science, optimization, and drug discovery. By integrating theoretical foundations with experimental advancements, this article aims to provide a clear and accessible reference for students, researchers, and scientists entering the rapidly evolving field of quantum computing.
Quantum systems promise to revolutionize information processing science and technology [1-3]. The preservation of quantum coherence, the defining property of qubits, fundamentally constrains the performance of quantum information processing with quantum memories [4]. While trapped atomic ions theoretically support million-year coherence based on spontaneous emission [5-7], experimental demonstrations have reached far less, only about an hour [8-13]. Here we combine clock-state qubits with decoherence-free subspace (DFS) encoding to achieve coherence exceeding ten hours. Using correlation-based phase tracking in 171Yb+ ion pairs sympathetically cooled by 138Ba+ ion, we demonstrate this without magnetic shielding or enhanced microwave phase stabilization that previously limited coherence times. DFS encoding references the qubit phase to the inter-ion energy difference to reject microwave phase noise and common-mode magnetic fluctuations, while clock states provide environmental insensitivity. Throughout measurements extended to 1600 seconds, we observe minimal coherence decay, with exponential fits yielding a coherence time of (3.77 +/- 1.09) x 10^4 seconds. Our results establish DFS encoding as a form of passive error correction that eliminates technical noise constraints, unlocking the million-year coherence potential of atomic ions for scalable quantum information processing.
Abstract Landauer’s bound is applicable to irreversible quantum operations. In this study, we showcased that the Doppler temperature manifests the existence of Landauer’s bound, which does not block a spin from (irreversibly) flipping with a tiny amount of energy via quantum tunneling. Verified by a spin–spin magnetic interaction experiment, this (energy) amount was determined to be only 1.25 times the theoretical value of Landauer’s bound. Based on Heisenberg’s principle, we defined information from a measuring perspective: one bit of information corresponds to the smallest error when quantifying the product of the measured energy uncertainty ( ΔE Δ E ) and the measured time duration ( Δt Δ t ). We then illustrate an optically manipulated, spin-encoded, near-Landauer-bound, near-Heisenberg-limit quantum computer that encompasses this new definition of information. This study may represent the last piece of the puzzle in understanding both quantum Landauer erasure and Heisenberg’s quantum limit since a single spin is the smallest information carrier.